I am a PhD candidate in the Machine Learning Department at Carnegie Mellon University (CMU), where I am fortunate to be advised by Professor Tom Mitchell.

Previously, I spent three wonderful years at the Karlsruhe Institute of Technology, completing my bachelor's degree in Computer Science. I was a research assistant and part of the TECO research group led by Professor Michael Beigl. I am grateful for the mentorship of Professor Manuela Veloso who hosted me as a research intern as part of the CLICS scholarship program.

In the industry, I worked as a research intern at AWS where I designed new algorithms and contributed to the AutoGluon project.

My research interests are broadly in the areas of machine learning and optimization. Particularly, I am interested in the theoretical and practical problems that arise when deploying data-driven paradigms in real world settings such as education and healthcare. Currently, my research focuses on problems related to intelligent tutoring systems including student knowledge modeling, performance predictions, personalized curriculum design and emerging types of learning technologies.

Research opportunities: I am happy to collaborate, discuss research and answer questions about CMU's academic programs. If you are interested, please feel free to send me an email.

Visit Google Scholar for a complete and up-to-date list of publications.

Common curriculum analytics techniques assume time-invariant course behavior and are unable to capture temporal variations in the data. We introduce an item response theory (IRT)-based methodology to address the open problem of monitoring course difficulty changes over time to ensure the equal treatment of student cohorts and GPA consistency.

Conversational tutoring systems (CTSs) promote cognitive engagement and benefit learning outcomes. Still, the time and cost required to author CTS content is a major obstacle to widespread adoption. We leverage LLMs as foundation for a novel type of CTSs that can automatically induce and orchestrate coherent free-form adaptive dialogues.

We employ a multi-armed bandit framework inside an online tutoring system to learn how to provide students with effective hints. Using data from over 190,000 students, we quantify effects on various learning outcome measures. A live use evaluation of the optimized assistance policies showed significant improvements in student learning outcomes.

We propose transfer learning techniques that can mitigate the student performance modeling cold-start problem for new courses by leveraging log data from existing courses. Our course-agnostic models enable accurate predictions for future courses when they are first deployed.

We study how to utilize various types of student log data for performance modeling using four recent large-scale datasets. We propose various extensions over earlier methods and define a new state of the art for logistic regression-based performance modeling.

We use a pan-cancer pathway model to identify novel combination therapies by defining multiple treatment optimization problems and solving them by combining CMA-ES with an efficient Hamiltonian Monte-Carlo sampling scheme. We also consider sequential treatment plans.

We propose the first algorithm for the bandit linear optimization problem for tree-form sequential decision making that offers both (i) linear-time iterations (in the size of the decision tree) and (ii) O(√T) cumulative regret in expectation compared to any fixed strategy, at all times T.

Teaching Assistant in Spring 2022.

Teaching Assistant in Fall 2020.